@inproceedings{cf96b729e6f548d8ba4e2e75c93bb7c5,
title = "Man woman detection in surveillance images",
abstract = "Human gender detection from body profile is an important task for surveillance. Most surveillance cameras are placed at a distance such that it is not possible to see people's face clearly. In this paper, we report the comparison between fast-feature pyramids and deep region-based convolutional neural network (RCNN) to detect a person in surveillance images. Since RCNN performs better in detecting a person, further training is applied to the RCNN to detect man and woman. Transfer learning strategy is used due to a small number of training images. The result shows that the trained RCNN can detect man and woman with promising result.",
keywords = "detection, fast feature pyramids, gender, man woman, region based CNN, transfer learning",
author = "Dina Chahyati and Fanany, {Mohamad Ivan} and Arymurthy, {Aniati Murni}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Information and Communication Technology, ICoIC7 2017 ; Conference date: 17-05-2017 Through 19-05-2017",
year = "2017",
month = oct,
day = "18",
doi = "10.1109/ICoICT.2017.8074682",
language = "English",
series = "2017 5th International Conference on Information and Communication Technology, ICoIC7 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 5th International Conference on Information and Communication Technology, ICoIC7 2017",
address = "United States",
}